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VMEA: studies on replacing strategies and diversity in dynamic environments

Published: 07 July 2007 Publication History

Abstract

We investigate some improvements to a memory-based evolutionary algorithm already studied with success in dynamic optimization problems. Two new replacing strategies to incorporate in the algorithm are proposed and a comparative study with previous approaches is made. The results show that the studied mechanisms powerfully improve the efficiency and the adaptability of the evolutionary algorithm.

References

[1]
A. Simões and E. Costa. Variable-size Memory Evolutionary Algorithm to Deal with Dynamic Environments. Applications of Evolutionary Computing, LNCS 4448, 2007. Berlin: Springer-Verlag.
[2]
A. Simões and E. Costa. VMEA: studies of the impact of different replacing strategies in the algorithm's performance an in the population's diversity when dealing with dynamic environments. CISUC TR 2007/001, ISSN 0874--338X, February 2007.

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  • (2009)Dynamic Problems and Nature Inspired Meta-heuristicsBiologically-Inspired Optimisation Methods10.1007/978-3-642-01262-4_4(79-109)Online publication date: 2009

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    cover image ACM Conferences
    GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
    July 2007
    2313 pages
    ISBN:9781595936974
    DOI:10.1145/1276958

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    New York, NY, United States

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    Published: 07 July 2007

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    Author Tags

    1. diversity
    2. dynamic environments
    3. memory
    4. replacing strategies

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    GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    • (2009)Dynamic Problems and Nature Inspired Meta-heuristicsBiologically-Inspired Optimisation Methods10.1007/978-3-642-01262-4_4(79-109)Online publication date: 2009

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